Information Bottleneck Measurement for Compressed Sensing Image Reconstruction
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Bokyeung | - |
dc.contributor.author | Ko, Kyungdeuk | - |
dc.contributor.author | Hong, Jonghwan | - |
dc.contributor.author | Ku, Bonhwa | - |
dc.contributor.author | Ko, Hanseok | - |
dc.date.accessioned | 2022-11-16T05:41:00Z | - |
dc.date.available | 2022-11-16T05:41:00Z | - |
dc.date.created | 2022-11-15 | - |
dc.date.issued | 2022 | - |
dc.identifier.issn | 1070-9908 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/145575 | - |
dc.description.abstract | Image Compressed Sensing (CS) has achieved a lot of performance improvement thanks to advances in deep networks. The CS method is generally composed of a sensing and a decoder. The sensing and decoder networks have a significant impact on the reconstruction performance, and it is obvious that both two networks must be in harmony. However, previous studies have focused on designing the loss function considering only the decoder network. In this paper, we propose a novel training process that can learn sensing and decoder networks simultaneously using Information Bottleneck (IB) theory. By maximizing importance through proposed importance generator, the sensing network is trained to compress important information for image reconstruction of the decoder network. The representative experimental results demonstrate that the proposed method is applied in recently proposed CS algorithms and increases the reconstruction performance with large margin in all CS ratios. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.subject | NETWORKS | - |
dc.title | Information Bottleneck Measurement for Compressed Sensing Image Reconstruction | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Ko, Hanseok | - |
dc.identifier.doi | 10.1109/LSP.2022.3205275 | - |
dc.identifier.scopusid | 2-s2.0-85137865574 | - |
dc.identifier.wosid | 000854612000004 | - |
dc.identifier.bibliographicCitation | IEEE SIGNAL PROCESSING LETTERS, v.29, pp.1943 - 1947 | - |
dc.relation.isPartOf | IEEE SIGNAL PROCESSING LETTERS | - |
dc.citation.title | IEEE SIGNAL PROCESSING LETTERS | - |
dc.citation.volume | 29 | - |
dc.citation.startPage | 1943 | - |
dc.citation.endPage | 1947 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.subject.keywordPlus | NETWORKS | - |
dc.subject.keywordAuthor | Sensors | - |
dc.subject.keywordAuthor | Generators | - |
dc.subject.keywordAuthor | Decoding | - |
dc.subject.keywordAuthor | Training | - |
dc.subject.keywordAuthor | Image reconstruction | - |
dc.subject.keywordAuthor | Image coding | - |
dc.subject.keywordAuthor | Loss measurement | - |
dc.subject.keywordAuthor | Information bottleneck | - |
dc.subject.keywordAuthor | image compressed sens- ing | - |
dc.subject.keywordAuthor | deep learning | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(02841) 서울특별시 성북구 안암로 14502-3290-1114
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.